Hydraulic turbine cavitation acoustic signal identification method based on big data machine learning

ABSTRACT

The present invention provides a hydraulic turbine cavitation acoustic signal identification method based on big data machine learning. According to the method, time sequence clustering based on multiple operating conditions under the multi-output condition of the hydraulic turbine set is performed by utilizing an neural network, characteristic quantities of the hydraulic turbine set under a steady condition in a healthy state is screened; a random forest algorithm is introduced to perform feature screening of multiple measuring points under steady-state operation of the hydraulic turbine set, optimal feature measuring points and optimal feature subsets are extracted, finally a health state prediction model is constructed by using gated recurrent units; whether incipient cavitation is present in the equipment is judged. The present invention can effectively identify the occurrence of incipient cavitation in the hydraulic turbine set, reducing unnecessary shutdown of the equipment and prolonging the service life.

TECHNICAL FIELD

The present invention relates to the field of fault recognition andearly warning, in particular to a hydraulic turbine cavitation acousticsignal identification method based on big data and machine learning.

BACKGROUND

Hydropower is an important part of the global energy strategy. Amongrenewable resources, hydropower is the highest in electric energyproduction, accounting for 16-17% of the world's gross generation andabout 80% of the world's renewable electricity. The hydraulic turbinegenerator set is the core equipment of a hydropower station, and thesteady operation of the set is of great significance to avoid potentialsafety hazards and improve the economic benefits of power generationenterprises. Cavitation is a common failure phenomenon in hydraulicmachinery. The existence of cavitation will reduce the set efficiencyand increase the set vibration and blade wear, resulting in greatlyreduced working life of the hydraulic turbine set and causing hugeeconomic losses.

There is usually a transitional development period of deterioration froma steady state to cavitation. Conventional cavitation state detectiontechnologies usually require complicated physical model or rely on theexperiential knowledge of experts. Moreover, there are problems such asa few monitoring points, and lack of systematicness andcomprehensiveness. Diagnosis is usually made when the monitoring sitedata exceed the fault alarm threshold, so the prediction & diagnosis andregulation & maintenance of incipient cavitation cannot be performedtimely in the early stage.

With the rise of artificial intelligence represented by neural network,the nonlinear parameters and signals can be well fitted by the neuralnetwork and applied in early fault diagnosis and warning. However, howto use the neural network to identify and capture the cavitation signalsof a hydraulic turbine is still an urgent technical problem.

SUMMARY

The present invention is intended to solve the problems in the priorart, and provides a hydraulic turbine cavitation acoustic signalidentification method based on big data learning, which can effectivelyidentify the occurrence of incipient cavitation in the hydraulic turbineset and timely provide warning for maintenance, thereby reducingunnecessary shutdown of the equipment and prolonging the service life.

To achieve the aforesaid purposes, the present invention adopts thefollowing technical solutions:

A hydraulic turbine cavitation acoustic signal identification methodbased on big data learning includes the following steps:

S1, obtaining latest acoustic signal time sequence data of eachmeasuring point in real time through measuring points arranged on ahydraulic turbine set, and partitioning the acoustic signal timesequence data of each measuring point into multiple normalized acousticsignal subsequences, wherein a latest recorded acoustic signalsubsequence of each measuring point is used as a real-time signalsubsequence;

S2, inputting the acoustic signal subsequences of all measuring pointsobtained in S1 into a self-organizing maps (SOM) neural network,clustering the acoustic signal subsequences into multiple clustersaccording to the corresponding operating condition of the hydraulicturbine set, and then dividing the clusters into a steady-state clusterand an unsteady-state cluster according to a signal fluctuation degreeof the acoustic signal subsequences in each cluster;

S3, traversing distribution of the real-time signal subsequences of allmeasuring points in the clusters; if a number of the real-time signalsubsequences contained in the steady-state cluster is not lower than aminimum number threshold, it is judged that the hydraulic turbine is ina steady condition and incipient cavitation warning proceeds accordingto S4-S8; otherwise, the current incipient cavitation warning process isinterrupted;

S4, performing feature screening on the real-time signal subsequencescontained in the steady-state cluster by a RF algorithm, and extractingoptimal feature measuring points which can sensitively reflect changesin the operating condition of the hydraulic turbine set and optimalfeature subsets of each optimal feature measuring point;

S5, normalizing the optimal feature subsets of each optimal featuremeasuring point and calculating information entropy, and with theinformation entropy as an input, predicting a future trend of thehydraulic turbine set in a healthy state by using a health stateprediction model constructed based on multilayer gate recurrent units(GRUs) to obtain predictive information entropy of the acoustic signalof each optimal feature measuring point in the next predictive step;

S6, obtaining acoustic signal time sequence data actually acquired fromeach optimal feature measuring point on the hydraulic turbine set in thenext predictive step and calculating actual information entropy, andcalculating a dynamic tolerance of each optimal feature measuring pointfrom the predictive information entropy and the actual informationentropy;

S7, based on the current output condition of the hydraulic turbine set,obtaining acoustic signal information entropy (with incipient cavitationpresent) of the hydraulic turbine set in the next predictive stepthrough prediction using the pre-constructed SOM network, andcalculating a dynamic tolerance alarm threshold of each optimal featuremeasuring point from the predictive information entropy and the acousticsignal information entropy (with incipient cavitation present); and

S8, comparing a sum of the dynamic tolerances of all optimal featuremeasuring points with a sum of the dynamic tolerance alarm thresholdsbased on a threshold method, and judging whether the incipientcavitation occurs to the hydraulic turbine set; if yes, an incipientcavitation warning is given; otherwise, no incipient cavitation warningis given.

Preferably, in S1, the method of partitioning the acoustic signal timesequence data of each measuring point into multiple acoustic signalsubsequences includes the following steps:

S11, performing fixed-step sliding through a fixed-sized time window onthe acoustic signal time sequence data of each measuring point, andextracting an acoustic signal subsequence from the time window everytime one step is slided by; and

S12, normalizing each acoustic signal subsequence extracted in S11 toobtain a finally outputted acoustic signal subsequence.

Preferably, the implementation method of S2 includes the followingsteps:

S21, inputting the acoustic signal subsequences of all measuring pointsobtained in S1 as an input layer of the SOM neural network, so that theinputted acoustic signal subsequences are divided into differentclusters through unsupervised learning clustering; and

S22, for each cluster clustered in S21, calculating multiple statisticalvalues of data points in each acoustic signal subsequence, and thencalculating a deviation of each statistical value of different acousticsignal subsequences in the same cluster; if the deviation of eachstatistical value corresponding to one cluster is less than therespective deviation threshold, such cluster is marked as a steady-statecluster; otherwise, such cluster is marked as an unsteady-state cluster.

Preferably, the multiple statistical values include the mean value,maximum value, minimum value and median of the data points in theacoustic signal subsequences, and the deviation is a variance.

Preferably, the implementation method of S4 includes the followingsteps:

S41, performing a first disturbance on each real-time signal subsequencecontained in the steady-state cluster based on a RF algorithm, andcalculating a feature importance index Ψ_(k) of each correspondingmeasuring point according to the results before and after thedisturbance, wherein a calculation formula is as follows:

$\Psi_{k} = {\frac{1}{B}{\sum\limits_{b = 1}^{B}\left( {R_{b}^{oob} - R_{bk}^{oob}} \right)}}$

where B represents a total number of the real-time signal subsequencescontained in the steady-state cluster, R_(b) ^(00b) represents a numberof correctly classified out-of-bag (OOB) data of a decision-making treebefore the first disturbance is performed on the bth real-time signalsubsequence, and R_(bk) ^(00b) represents a number of correctlyclassified OOB data of a decision-making tree after the firstdisturbance is performed on the bth real-time signal subsequence;

S42, based on the feature importance index of each measuring pointobtained in S41, screening optimal feature measuring points which cansensitively reflect changes in the operating condition of the hydraulicturbine set from the measuring points corresponding to all real-timesignal subsequences in the steady-state cluster;

S43, for each optimal feature measuring point, performing empirical modedecomposition (EMD) on a corresponding real-time signal subsequence toobtain an equal number of feature subsets, and then performing a seconddisturbance on each feature subset based on the RF algorithm, andcalculating a feature importance index Ψ_(k1) of each correspondingfeature subset according to the results before and after thedisturbance, wherein a calculation formula is as follows:

$\Psi_{k1} = {\frac{1}{B^{\prime}}{\sum\limits_{b^{\prime} = 1}^{B^{\prime}}\left( {R_{b^{\prime}}^{\prime{oob}} - R_{b^{\prime}k}^{\prime{oob}}} \right)}}$

where B′ represents a number of feature subsets of each optimal featuremeasuring point, R′_(b′) ^(oob) represents a number of correctlyclassified OOB data of a decision-making tree before the seconddisturbance is performed on the b′th feature subset, and R′_(b′k) ^(oob)represents a number of correctly classified OOB data of adecision-making tree after the second disturbance is performed on theb′th feature subset; and

S44, based on the feature importance index of each feature subsetobtained in S43, screening an equal number of optimal feature subsetswhich can sensitively reflect changes in the operating condition of thehydraulic turbine set from the feature subsets of each optimal featuremeasuring point.

Preferably, the implementation method of S5 includes the followingsteps:

S51, for any pth optimal feature measuring point, combining its optimalfeature subsets to form a signal sequence X′=[x_(p,1),x_(p,2), . . . ,x_(p,r), . . . , x_(p,R)] with a length R after the feature extraction,normalizing the signal sequence X′ to obtain a signal sequenceY′=[y′_(p,1),y′_(p,2), . . . , y′_(p,R)], and then calculatinginformation entropy y_(p,R) of the normalized signal sequence Y′:

$y_{p,R} = {- {\sum\limits_{r = 1}^{R}{y_{p,r}^{\prime}{\lg\left( y_{p,r}^{\prime} \right)}}}}$

S52, constructing and training a health state prediction model composedof multilayer GRUs, wherein each GRU is corresponding to an optimalfeature measuring point, configured to predict a future trend ofacoustic signal sequence information entropy at a corresponding optimalfeature measuring point when the hydraulic turbine set is in a healthycondition without cavitation; and

S53, inputting the information entropy y_(p,R) of each optimal featuremeasuring point calculated in S51 into the corresponding GRU in thehealth state prediction model successively, wherein the GRU outputspredictive information entropy y_(p,h) of the acoustic signal of thecorresponding optimal feature measuring point in the next time step h tobe predicted.

Preferably, in S6, for any pth optimal feature measuring point, itsdynamic tolerance Δy_(p,h) is calculated by the following formula:

${\Delta y}_{p,h} = {{{❘\frac{{\hat{y}}_{p,h} - y_{p,h}}{y_{p,h}}❘} \times 100}\%}$

where ŷ_(p,h) represents actual information entropy of the acousticsignal time sequence data actually acquired from the pth optimal featuremeasuring point in the time step h.

Preferably, the implementation method of S7 includes the followingsteps:

S71, based on the SOM network, constructing a multi-dimensional mappingnetwork between the acoustic signal information entropy of the measuringpoints and the cavitation state of the hydraulic turbine set underdifferent output conditions;

S72, determining the current output condition of the hydraulic turbineset, and then predicting acoustic signal information entropy y _(p,h)(with incipient cavitation present) of the optimal feature measuringpoints in the hydraulic turbine set in the time step h by using themulti-dimensional mapping network; and

S73, calculating a dynamic tolerance alarm threshold Δy _(p,h) accordingto the acoustic signal information entropy y _(p,h) and the predictiveinformation entropy y_(p,h) of each optimal feature measuring point,wherein a calculation formula is as follows:

${\Delta{\overset{\_}{y}}_{p,h}} = {{{❘\frac{{\overset{\_}{y}}_{p,h} - y_{p,h}}{y_{p,h}}❘} \times 100}{\%.}}$

Preferably, in S8, a comparative analysis is performed by an analyticalmethod of stacked area chart.

Preferably, S1-S8 proceed iteratively according to the set step intervalwhen the hydraulic turbine set is operating.

Compared with the prior art, the present invention features thefollowing beneficial effects:

Based on the SOM neural network subject to big data training and GRU,the real-time detection on acoustic signals of the hydraulic turbine setunder a steady and healthy condition is realized. By extracting thestate feature, the future short-term steady condition information can bepredicted and outputted in advance. After the predictive information iscompared with the real information, the occurrence of incipientcavitation in the hydroelectric equipment in operation can be foundtimely. The present invention can reduce the blindness of hydraulicturbine set repair, and improve the safety operation capability of theset.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of the hydraulic turbine cavitation acousticsignal identification method of the present invention.

FIG. 2 is a flow chart of the cavitation warning of the presentinvention.

FIG. 3 is a stacked area chart of dynamic tolerances of multiplemeasuring points.

FIG. 4 is a curve profile of cavitation coefficient-relative efficiency(σ−η′).

FIG. 5 is a change curve profile of overall dynamic tolerances.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The present invention will be further described below in combinationwith the accompanying drawings and specific embodiments.

In the present invention, based on the characteristics that a hydraulicturbine set will emit an abnormal acoustic signal when cavitationoccurs, the noninvasively real-time warning for incipient cavitation isrealized by monitoring the acoustic signals from different measuringpoint positions of the hydraulic turbine generator set. The solutionadopted by the present invention includes the following general process:first the acoustic signal data preprocessing is performed, clusteringanalysis is performed by using a Self-Organizing Maps (SOM) neuralnetwork, the operating state of the hydraulic turbine set is accuratelyidentified, and a RF algorithm is introduced to perform featureselection in steady state and extract the highly targeted featuresignals; the future vibration development of each monitoring point ofthe hydraulic turbine set is predicted by using the GRU, a dynamictolerance model is constructed to compare the dynamic differencesbetween the actual data and the predicted data in real timecontinuously, whether incipient cavitation trend occurs to the hydraulicturbine generator set is monitored by coupling the actual deviation gapsof multiple measuring points, and the cavitation in the workinghydroelectric equipment is found timely, so as to take maintenancemeasures properly. The specific implementation steps of the presentinvention will be described below for ease of understanding.

As shown in FIG. 1 , in a preferred embodiment of the present invention,a hydraulic turbine cavitation acoustic signal identification methodbased on big data learning is provide, as shown in S1-S8. The basicprocess to realize cavitation warning by identification of the overallhydraulic turbine cavitation acoustic signals is as shown in FIG. 2 .The steps are specifically described below.

S 1, the latest acoustic signal time sequence data of each measuringpoint are obtained in real time through measuring points arranged on ahydraulic turbine set, and the acoustic signal time sequence data ofeach measuring point are partitioned into multiple normalized acousticsignal subsequences, wherein a latest recorded acoustic signalsubsequence of each measuring point is used as a real-time signalsubsequence.

It should be noted that the acoustic signal measuring points on thehydraulic turbine set need to be arranged in advance, and the specificarrangement positions can depend on experts' experience. In theoperation process of the hydraulic turbine set, each measuring pointposition will uninterruptedly acquire acoustic signals emitted by thehydraulic turbine set according to a set sampling frequency through asensor, and store such signals in a form of time sequence, therebyforming massive time data. Moreover, in the operation process of thehydraulic turbine set, the incipient cavitation warning process isiterative at a set step interval, namely, the newly generated data areanalyzed at a certain step interval, realizing the incipient cavitationwarning and timely finding a sign of cavitation from the acousticsignals. Therefore, in each round of incipient cavitation warning, theacoustic signal time sequence data acquired from each measuring pointshould contain the latest recorded data.

In such example, the acoustic signal time sequence data of eachmeasuring point can be partitioned into multiple acoustic signalsubsequences by setting a time window. The specific method includes thefollowing steps:

S11, fixed-step sliding is performed by a fixed-sized time window on theacoustic signal time sequence data of each measuring point, and anacoustic signal subsequence is extracted from the time window every timeone step is slided by.

It is assumed that the acoustic signal time sequence data of any ithmeasuring point are denoted as x_(i)=[x_(i),1,x_(i),2, . . . , x_(i,m),. . . , x_(i,M)] wherein M represents a length of the acoustic signaltime sequence data, x_(i,m) represents the mth sampling point data inthe acoustic signal time sequence data of the ith measuring point, i=1,2, 3, . . . , I, and I represents a total number of measuring points onthe hydraulic turbine set. During partitioning and extraction of x_(i),sliding can be performed at a step length k through a time window with alength w, wherein the first acoustic signal subsequenceθ₁=[x_(i),1,x_(i),2, . . . , x_(i,w)] is obtained by the first step, andso on, to form a sample data set φ=<θ₁,θ₂, . . . , θ_(c)> composed of aseries of acoustic signal subsequences, where C represents a totalnumber of acoustic signal subsequences extracted from an acoustic signaltime sequence datum.

A latest recorded acoustic signal subsequence θ_(c) in each measuringpoint is denoted as a real-time signal subsequence, and such subsequencewill be used as a basis of cavitation warning later.

S12, each acoustic signal subsequence θ_(j) (j=1, . . . , C) extractedin S11 is normalized to obtain a finally outputted acoustic signalsubsequence θ′_(j), wherein a L1 norm can be adopted for normalization,and a formula is as follows:

$\theta_{j}^{\prime} = \frac{\theta_{j}}{\theta_{j}}$

where ∥⋅∥ represents a norm of a matrix.

Hence, the sample data set φ is normalized to φ′=<θ′₁,θ′₂, . . . ,θ′_(c)>, wherein θ′_(c) represents a normalized acoustic signalsubsequence and will participate in the follow-up warning process.

There is a certain unsteady period after the hydraulic turbine set isstarted, and the working condition gradually tends to be steady later.Therefore, the purpose of extracting the acoustic signal subsequences isto form a series of samples at different periods in the operationprocess of the hydraulic turbine set, and such samples represent changesof acoustic signal data under different working conditions. However, theincipient cavitation for warning in the present invention occurs in thefollow-up stage after the steady working condition. Therefore, it isnecessary to first identify whether the hydraulic turbine set hasentered the steady working condition. Such identification function isrealized through follow-up clustering.

S2, the acoustic signal subsequences of all measuring points obtained inS1 are inputted into a SOM neural network, the acoustic signalsubsequences are clustered into multiple clusters according to thecorresponding operating condition of the hydraulic turbine set, and thenthe clusters are divided into a steady-state cluster and anunsteady-state cluster according to a signal fluctuation degree of theacoustic signal subsequences in each cluster.

SOM, as an unsupervised learning algorithm for clustering andhigh-dimensional visualization, is an artificial neural networkdeveloped by simulating the characteristics of human brain in signalprocessing. In the process of clustering, the SOM will classify dataaccording to data features without specifying cluster categories inadvance. Therefore, the SOM neural network is used for unsupervisedlearning clustering in the present invention. In such example, thespecific implementation method of S2 includes the following steps:

S21, the acoustic signal subsequences of all measuring points obtainedin S1 are inputted as an input layer of the SOM neural network, so thatthe inputted acoustic signal subsequences are divided into differentclusters through unsupervised learning clustering.

The SOM algorithm belongs to the prior art, and includes the followinggeneral process: initialization, competition, cooperation, adaptationand iteration. The implementation process is briefly described below,but it should be noted that this is only for ease of understanding,rather than serving as a specific limitation to the present invention.The SOM algorithm includes the following process:

1) the normalized φ′=<θ′₁, θ′₂, . . . , θ′_(c)> is used as an inputlayer of the SOM neural network;

2) weight vectors are initialized with a minimum random value to obtaina weight vector W_(j) of each neuron, wherein j represents the jthneuron, j=1, 2, 3, . . . , J, and J represents a total number ofneurons; then the weight vectors W₁ are normalized to obtain anormalized weight vector W′_(j);

3) an initial learning rate η(t) is set as η(0);

4) a Euclidean distance of each neuron N_(i)(t) node is calculated, anda node with a minimum distance is selected as a winning node; N_(j)*(t)is defined as a winning neuron, j* represents a serial number where thewinning neuron is located, and t represents a number of iterations (aninitial value is 0); a winning neuron is denoted as 1, a non-winningneuron is denoted as 0, and weights of all nodes in N_(j)*(t) areupdated according to a gradient descent method; and

-   -   5) the learning rate η(t) and the winning neuron are updated;        when the number of iterations t exceeds a set number of        iterations K, the cycle ends; otherwise, the number of        iterations is set as t+1, and the process skips to 4).

S22, for each cluster clustered in S21, multiple statistical values ofdata points in each acoustic signal subsequence are calculated, and thena deviation of each statistical value of different acoustic signalsubsequences in the same cluster is calculated; if the deviation of eachstatistical value corresponding to one cluster is less than therespective deviation threshold, such cluster is marked as a steady-statecluster; otherwise, such cluster is marked as an unsteady-state cluster.

It should be noted that the above-mentioned statistical values can beany statistic that reflects data fluctuations, such as mean value,maximum value, minimum value and median, or a combination of some.Moreover, the corresponding statistical values will be calculated fromeach acoustic signal subsequence based on the sampling point datatherein, and deviations of such statistical values can reflect afluctuation of different acoustic signal subsequences in a cluster. Insuch example, the adopted multiple statistical values include the meanvalue, maximum value, minimum value and median of the data points in theacoustic signal subsequences, and the deviations of such statisticalvalues are variances between statistical values. For example, for acluster, it is assumed that there are q acoustic signal subsequences,then a mean value, a maximum value, a minimum value and a median arecalculated respectively from each acoustic signal subsequence, and thena variance of the mean values of such q acoustic signal subsequences, avariance of the maximum values of such q acoustic signal subsequences, avariance of the minimum values of such q acoustic signal subsequences,and a variance of the medians of such q acoustic signal subsequences arecalculated respectively; it is assumed that such four variances are lessthan the respective variance thresholds of such four statistical values,such cluster is considered a steady-state cluster; if the variance ofany statistical value exceeds the threshold, such cluster is consideredan unsteady-state cluster. Besides, the variance threshold of eachstatistical value can be determined by statistical analysis based onhistorical data.

S3, distribution of the real-time signal subsequences θ′c of allmeasuring points in the clusters is traversed; if a number θ′c of thereal-time signal subsequences contained in the steady-state cluster isnot lower than a minimum number threshold, it is judged that thehydraulic turbine set is in a steady condition and incipient cavitationwarning proceeds according to S4-S8; otherwise, it indicates that thehydraulic turbine set is not in a steady condition, and it ismeaningless to perform cavitation warning at this time, so the currentincipient cavitation warning process is interrupted, and a nextincipient cavitation warning can be performed after new data aregenerated.

It should be noted that such minimum number threshold I_(T) depends on anumber of measuring points. If a total number I of measuring points islow, I_(T) should be as close to I as possible, namely, all measuringpoints should be in a steady condition as far as possible to satisfy thedata requirements of follow-up prediction. If the total number I ofmeasuring points is high, I_(T) is just a certain percentage of I. Insuch example, if the total number I of measuring points is less than 10,the minimum number threshold is I_(T)=I; if the total number I ofmeasuring points is not less than 10, the minimum number threshold isI_(T)=0.8I.

The specific implementation form of follow-up S4-S8 in the incipientcavitation warning process of the present invention is described below.

S4, feature screening is performed on the real-time signal subsequencescontained in the steady-state cluster by a RF algorithm, and optimalfeature measuring points which can sensitively reflect changes in theoperating condition of the hydraulic turbine set and optimal featuresubsets of each optimal feature measuring point are extracted.

The RF algorithm is a highly flexible machine learning algorithm whichcan evaluate the importance of each feature in the classification. Notall sequence samples in the real-time signal subsequences contained inthe steady-state cluster have great influence on the final cavitationwarning of the hydraulic turbine. Therefore, it is necessary to performscreening on the sequence samples to obtain features which cansensitively reflect changes in the operating condition of the hydraulicturbine set, and such method includes two steps: the first step isscreening on the measuring points, and for the sake of description, thescreened measuring points are called optimal feature measuring points;the second step is screening on feature subsets in the optimal featuremeasuring points, and for the sake of description as well, the screenedfeature subsets in the optimal feature measuring points are calledoptimal feature subsets.

In such example, the specific implementation method of S4 includes thefollowing steps:

S41, a disturbance (for distinguishing, such disturbance is denoted as afirst disturbance; refer to RF algorithm for a specific disturbancemethod, which will not be repeated herein) is performed on eachreal-time signal subsequence contained in the steady-state cluster basedon a RF algorithm, and a feature importance index Ψ_(k) of eachcorresponding measuring point is calculated according to the resultsbefore and after the disturbance, wherein a calculation formula is asfollows:

$\Psi_{k} = {\frac{1}{B}{\sum\limits_{b = 1}^{B}\left( {R_{b}^{oob} - R_{bk}^{oob}} \right)}}$

where B represents a number of RF training samples, i.e., a total numberof the real-time signal subsequences contained in the steady-statecluster, R_(b) ^(oob) represents a number of correctly classified OOBdata of a decision-making tree before the first disturbance is performedon the bth real-time signal subsequence, and R_(bk) ^(oob) represents anumber of correctly classified OOB data of a decision-making tree afterthe first disturbance is performed on the bth real-time signalsubsequence.

S42, each real-time signal subsequence in the steady-state clustercorresponds to a measuring point, so the importance of the measuringpoints is ranked based on the feature importance index Ψ_(k1) of eachmeasuring point obtained in S41, and the smaller the Ψ_(k), the moreimportant the measuring points; part of the most important measuringpoints are screened from the measuring points corresponding to allreal-time signal subsequences in the steady-state cluster, used asoptimal feature measuring points and can sensitively reflect changes inthe operating condition of the hydraulic turbine set. The specificscreening method can be referred to the RF algorithm and will not beexpanded. A number of the optimal feature measuring points extracted insuch step is denoted as P.

S43, for each optimal feature measuring point, its correspondingreal-time signal subsequence is subjected to EMD to obtain n featuresubsets MF₁-MF_(n) (n represents a total number of feature subsets, anddifferent optimal feature measuring points have the same n); then adisturbance (for distinguishing, such disturbance is denoted as a seconddisturbance; refer to the RF algorithm for is specific disturbancemethod, which will not be repeated herein) is performed on each featuresubset based on the RF algorithm as well, and a feature importance indexΨ_(k1) of each corresponding feature subset is calculated according tothe results before and after the disturbance, wherein a calculationformula is as follows:

$\Psi_{k1} = {\frac{1}{B^{\prime}}{\sum\limits_{b^{\prime} = 1}^{B^{\prime}}\left( {R_{b\prime}^{\prime{oob}} - R_{b{\prime k}}^{\prime{oob}}} \right)}}$

where B′ represents a number of RF training samples, i.e., a number offeature subsets of each optimal feature measuring point, R′_(b′) ^(oob)represents a number of correctly classified OOB data of adecision-making tree before the second disturbance is performed on theb′th feature subset, and R′_(b′k) ^(oob) represents a number ofcorrectly classified OOB data of a decision-making tree after the seconddisturbance is performed on the b′th feature subset.

S44, the importance of the feature subsets is ranked based on thefeature importance index Ψ_(k1) of each feature subset obtained in S43,and the smaller the Ψ_(k1), the more important the feature subsets; partof the most important feature subsets are screened from all featuresubsets of each optimal feature measuring point, used as optimal featuresubsets which can sensitively reflect changes in the operating conditionof the hydraulic turbine set. It should be noted that the optimalfeature subsets screened from different optimal measuring points shouldhave an equal number of feature subsets.

S5, the optimal feature subsets of each optimal feature measuring pointare normalized and information entropy is calculated, and with theinformation entropy as an input, a future trend of the hydraulic turbineset in a healthy state is predicted by using a health state predictionmodel constructed based on multilayer GRUs to obtain predictiveinformation entropy of the acoustic signal of each optimal featuremeasuring point in the next predictive step.

In such example, the implementation method of S5 includes the followingsteps:

S51, for any pth optimal feature measuring point, its optimal featuresubsets are combined to form a signal sequence X′=[x_(p,1),x_(p,2), . .. , x_(p,r), . . . , x_(p,R)] with a length R after the featureextraction, normalizing the signal sequence X′ to obtain a signalsequence Y′=[y′_(p,1),y′_(p,2), . . . , y′_(p,R)], and then informationentropy y_(p,R) of the normalized signal sequence Y′ is calculated:

$y_{p,R} = {- {\sum\limits_{r = 1}^{R}{y_{p,r}^{\prime}{\lg\left( y_{p,r}^{\prime} \right)}}}}$

S52, a health state prediction model composed of multilayer GRUs isconstructed and trained, wherein each GRU is corresponding to an optimalfeature measuring point, configured to predict a future trend ofacoustic signal sequence information entropy at a corresponding optimalfeature measuring point when the hydraulic turbine set is in a healthycondition without cavitation; and

S53, the information entropy y_(p,R) of each optimal feature measuringpoint calculated in S51 is inputted into the corresponding GRU in thehealth state prediction model successively, wherein the GRU outputspredictive information entropy y_(p,h) of the acoustic signal of thecorresponding optimal feature measuring point in the next time step h tobe predicted.

The GRU is a kind of Recurrent Neural Network (RNN), which is proposedto solve the problems of gradient, etc. in long-term memory and backpropagation. A GRU model has two gates, i.e., update gate and resetgate. The prediction process in the GRU belongs to the prior art, and isdescribed as follows for ease of understanding:

First, the information entropy y_(p,R) is inputted as an initial inputset of GRU, and an update gate ξ is calculated by the following formula:

ξ_(h)=σ(Φ)(⁸⁶)h+U(^(ξ))y _(p,(h−1)))

where y_(p,h−1) stores the data information of a previous time step(h−1), h represents a next time step to be predicted, a represents aSigmoid activation function, and Φ(^(ξ)) and U^((ξ)) respectivelyrepresent a weight matrix inputted to the update gate ξ_(h) and a weightmatrix hidden to the update gate ξ_(h) in the previous time step.

Then, a reset gate γ_(h) is calculated by the following formula:

γ_(h)=τ(Φ)^((γ)) h+U ^((γ)) y _(p,h−1))

where Φ^((γ)) and U^((γ)) respectively represent a weight matrixinputted to the reset gate γ_(h) and a weight matrix hidden to the resetgate γ_(h) in the previous time step (h−1); in the update gate and resetgate, both h and y_(p,h−1) are multiplied by the weight matrixes, andΦ^((γ))h+U^((γ))y_(p,h−1) are added and then multiplied by the Sigmoidactivation function to realize normalized compression of the activationresult.

Then, a new memory content y′_(p,h) will use the reset gate to store therelated previous information, with a calculation formula as follows:

h′ _(ph)=tanh (Φ)^((γ)) h+γ _(h) ⊙U ^((γ)) y _(,hp))

A Hadamard product of the reset gate γ_(h) and U^((γ))y_(p,h−1) iscalculated to determine the previous information to retain and forget.

Finally, the network needs to calculate the data information of the timestep h stored by y_(p,h), and such vector will retain information of thecurrent unit and pass it on to the next unit; the update gate is used;the update gate decides the current memory content y′_(p,h) and theinformation to be collected in the previous time step y_(p,h−1), whereiny_(p,h) is calculated by the following formula:

y _(p,h)=ξ^(h) ⊙y _(p,(h−1))+(1−_(h))⊙Y′ _(p,h).

It should be noted that the health state prediction model needs to betrained in advance, so that each GRU can accurately predict theinformation entropy y_(p,h) in the next time step h based on theinformation entropy y_(p,R). During training, an Adam algorithm isadopted to adaptively and dynamically adjust a model learning rate, amean absolute percentage error is minimized as a target loss function,and a result is calculated based on the target loss function to detectwhether the GRU model accuracy reaches the requirements. If therequirements are reached, the training is completed; otherwise,optimizing is continued.

S6, acoustic signal time sequence data actually acquired from eachoptimal feature measuring point on the hydraulic turbine set is obtainedin the next predictive step h and actual information entropy ŷ_(p,h) iscalculated, and a dynamic tolerance Δy_(p,h) of each optimal featuremeasuring point is calculated from the predictive information entropyy_(p,h) and the actual information entropy ŷ_(p,h). For any pth optimalfeature measuring point, its dynamic tolerance Δy_(p,h) is calculated bythe following formula:

${\Delta y}_{p,h} = {{{❘\frac{{\hat{y}}_{p,h} - y_{p,h}}{y_{p,h}}❘} \times 100}\%}$

where ŷ_(p,h) represents actual information entropy of the acousticsignal time sequence data actually acquired from the pth optimal featuremeasuring point in the time step h.

It should be noted that the predictive information entropy y_(p,h)actually represents the information entropy corresponding to normalacoustic signals in the predictive step h when the hydraulic turbine setis in a healthy operating condition without cavitation. Moreover,ŷ_(p,h) represents the information entropy corresponding to the acousticsignals in the predictive step h when the current hydraulic turbine setis in an actual operating condition. At this time, it is unknown whetherthe incipient cavitation occurs to the hydraulic turbine set, butwhether the incipient cavitation occurs will affect ŷ_(p,h) and changethe dynamic tolerance Δy_(p,h). Therefore, whether the incipientcavitation occurs can be further judged according to the dynamictolerance Δy_(p,h) later.

S7, based on the current output condition of the hydraulic turbine set,acoustic signal information entropy ŷ_(p,h) (with incipient cavitationpresent) of the hydraulic turbine set in the next predictive step h ispredicted by using the pre-constructed SOM network, and a dynamictolerance alarm threshold Δy _(p,h) of each optimal feature measuringpoint is calculated by the predictive information entropy y_(p,h) andthe acoustic signal information entropy y _(p,h) (with incipientcavitation present). Hence, the dynamic tolerance alarm threshold Δy_(p,h) actually represents a value corresponding to the dynamictolerance when incipient cavitation occurs to the hydraulic turbine set.Based on such threshold, the existence of incipient cavitation can beinferred.

In such example, the specific implementation method of S7 includes thefollowing steps:

S71, based on the SOM network, a multi-dimensional mapping networkbetween the acoustic signal time-frequency feature of the measuringpoints and the cavitation state of the hydraulic turbine set underdifferent output conditions is constructed, wherein such acoustic signaltime-frequency feature is its information entropy; suchmulti-dimensional mapping network can be trained by historical data, sothat it can accurately predict the corresponding information entropyaccording to the output condition of the hydraulic turbine set.

S72, the current output condition of the hydraulic turbine set isdetermined, and then acoustic signal information entropy y _(p,h) (withincipient cavitation present) of the optimal feature measuring points inthe hydraulic turbine set in the time step h is predicted by using thetrained multi-dimensional mapping network; and

S73, a dynamic tolerance alarm threshold Δy _(p,h) is calculatedaccording to the acoustic signal information entropy y _(p,h) and thepredictive information entropy y_(p,h) of each optimal feature measuringpoint, wherein a calculation formula is as follows:

${\Delta{\overset{\_}{y}}_{p,h}} = {{{❘\frac{{\overset{\_}{y}}_{p,h} - y_{p,h}}{y_{p,h}}❘} \times 100}{\%.}}$

Through the adaptive coupling data, the dynamic tolerances and thedynamic tolerance alarm thresholds of multiple measuring points areevaluated, which can be used to judge whether the incipient cavitationoccurs to the equipment later.

S8, a sum of the dynamic tolerances of all optimal feature measuringpoints is compared with a sum of the dynamic tolerance alarm thresholds,and whether the incipient cavitation occurred in the hydraulic turbineset is judged, wherein the judgment rule is based on the thresholdmethod, namely, if the sum of the dynamic tolerances of all optimalfeature measuring points exceeds the sum of the dynamic tolerance alarmthresholds of all optimal feature measuring points, the incipientcavitation occurs, incipient cavitation warning is given, and the realincipient cavitation data are stored for subsequent analysis; otherwise,the hydraulic turbine set is considered to be in a normal conditionwithout incipient cavitation, and no incipient cavitation warning isgiven.

As the data of multiple optimal feature measuring points need to beadded and compared in such step, an analytical method of stacked areachart can be introduced for comparative analysis in order to facilitateanalysis and visual display. As shown in FIG. 3 , as an example, thedynamic tolerances of all the monitored measuring points in the timesequence can be mapped to a two-dimensional coordinate space, and thedynamic tolerances of all measuring points can be accumulated to obtaina change curve of overall dynamic tolerances of the hydraulic turbineset over time; similarly, the dynamic tolerance alarm thresholds can bemapped to the two-dimensional coordinate space as well, and the dynamictolerance alarm thresholds of all measuring points can be accumulated toobtain a change curve of overall dynamic tolerance alarm thresholds ofthe hydraulic turbine set over time. The judgment can be completed byintuitively observing the two change curves in the two-dimensionalcoordinate space. With five measuring points in FIG. 3 as an example, inthe position of h=4, the curve of overall dynamic tolerances exceeds thecurve of overall dynamic tolerance alarm thresholds, indicating that theincipient cavitation occurs.

The process of S1-S8 can be deemed as a round of incipient cavitationwarning process. The next round of incipient cavitation warning can beperformed when new data appear at each measuring point.

The hydraulic turbine cavitation acoustic signal identification methodshown as S1-S8 is applied to a specific example below to demonstrate itstechnical effects. The specific procedure of such method in thefollowing example is described above and will not be repeated herein.The specific implementation details and technical effects are mainlydemonstrated below.

EXAMPLE

In the example, a signal detection was performed on a cavitation processof an axial-flow hydraulic turbine under the conditions of maximum headand maximum output through a detection platform. As the cavitationusually occurs at a rotating wheel of the axial-flow hydraulic turbine,four measuring points were installed at a blade leading edge on an outerwall of the rotating wheel, a blade center line, a blade trailing edgeand an inlet of an exhaust water pipe to acquire transient acousticsignals, and such signals to form acoustic signal sequence data forwarning were recorded in real time.

A relative efficiency η′ of the hydraulic turbine is defined by thefollowing formula:

$\eta^{\prime} = \frac{\eta}{\eta_{Max}}$

where η_(Max) represents an efficiency value corresponding to a maximumcavitation coefficient, and η represents a measurement efficiency value.

A curve of cavitation coefficient-relative efficiency (σ-η′) for testunder such condition is as shown in FIG. 4 . In the figure,σ_(incipient) represents an initial cavitation coefficient, and at thistime, the cavitation is visible by naked-eye observation for the firsttime; σ_(critical) represents a critical cavitation coefficient, and atthis time, the efficiency drops by more than 1%. Therefore, in suchexample, the acoustic signal sequence data obtained under such conditionwere used for the incipient cavitation warning, and whether the methodof the present invention can give sensitive warning for the cavitationwas judged.

The specific incipient cavitation warning process was described inS1-S8, wherein the SOM neural network was adopted for time sequenceclustering. During extraction of the steady-state operating data,because only four measuring points were arranged in the test, featuresubsets were directly screened. By measuring the importance index offeature subsets, the feature subsets were ranked to select two featuresubsets (marked as feature subset 1 and feature subset 2) that cansensitively reflect changes in the operating condition of the hydraulicturbine set as optimal feature subsets for subsequent analysis. Theinformation entropy of the feature subset 1 and feature subset 2 of eachmeasuring point was extracted at a cavitation coefficient of 0.6-1.5,and the feature value information entropy of each measuring point waspredicted at a cavitation coefficient of 0.24-0.6. The results are asshown in Table 1.

TABLE 1 Information Entropy of Each Measuring Point σ 0.24 0.26 0.29 0.30.4 0.5 0.6 0.7 1 1.5 P1 (6.51, 6.23) (6.37, 6.33) (5.47, 6.96) (5.47,6.84) (5.87, 6.56) (5.90, 6.75) (5.76, 6.21) (5.31, 6.79) (5.02, 6.76)(4.51, 5.76) P2 (7.58, 6.39) (7.05, 6.48) (−5.95, 6.18)  (6.73, 6.79)(6.95, 6.43) (6.64, 6.50) (6.85, 6.17) (5.81, 6.87) (5.67, 6.92) (5.29,6.36) P3 (6.78, 6.82) (6.39, 6.69) (6.51, 6.09) (6.08, 6.28) (6.23,6.41) (6.66, 6.77) (6.14, 6.52) (5.17, 6.06) (5.34, 6.13) (4.34, 6.15)P4 (6.44, 6.43) (6.36, 6.42) (6.54, 6.82) (5.54, 6.82) (6.45, 6.34)(6.81, 7.01) (6.21, 6.58) (5.49, 6.65) (5.12, 5.95) (4.98, 5.49)

Finally, through the threshold method, a sum of the dynamic tolerancesof the four measuring points was compared with a sum of the dynamictolerance alarm thresholds to judge whether the incipient cavitationoccurs to the equipment. As shown in FIG. 5 , the results show that thesum of the dynamic tolerances increased dramatically when incipientcavitation occurred (σ_(i) =0.5), exceeding the sum of the dynamictolerance alarm thresholds, and triggering a cavitation warning alert,consistent with FIG. 4 . It indicates that the incipient cavitationwarning method provided by the present invention can find the cavitationin the working hydroelectric equipment timely through acoustic signals.

The above-mentioned embodiments are only used for describing thetechnical solutions of the present invention, rather than limiting.Although the present invention is described in detail by reference tothe above-mentioned embodiments, those of ordinary skill in the artshould understand that they can still make modifications to thetechnical solutions recorded in the above-mentioned embodiments, or makeequivalent substitutions to a part of or all technical characteristicsthereof; moreover, these modifications or substitutions will not makethe corresponding technical solutions depart from the scope of thetechnical solutions in the embodiments of the present invention.

1. A hydraulic turbine cavitation acoustic signal identification methodbased on big data learning, comprising the following steps: S1,obtaining latest acoustic signal time sequence data of each measuringpoint in real time through measuring points arranged on a hydraulicturbine set, and partitioning the acoustic signal time sequence data ofeach measuring point into multiple normalized acoustic signalsubsequences, wherein a latest recorded acoustic signal subsequence ofeach measuring point is used as a real-time signal subsequence; S2,inputting the acoustic signal subsequences of all measuring pointsobtained in S1 into a self-organizing maps (SOM) neural network,clustering the acoustic signal subsequences into multiple clustersaccording to the corresponding operating condition of the hydraulicturbine set, and then dividing the clusters into a steady-state clusterand an unsteady-state cluster according to a signal fluctuation degreeof the acoustic signal subsequences in each cluster; S3, traversingdistribution of the real-time signal subsequences of all measuringpoints in the clusters; if a number of the real-time signal subsequencescontained in the steady-state cluster is not lower than a minimum numberthreshold, it is judged that the hydraulic turbine is in a steadycondition and incipient cavitation warning proceeds according to S4-S8;otherwise, the current incipient cavitation warning process isinterrupted; S4, performing feature screening on the real-time signalsubsequences contained in the steady-state cluster by a random forest(RF) algorithm, and extracting optimal feature measuring points whichcan sensitively reflect changes in the operating condition of thehydraulic turbine set and optimal feature subsets of each optimalfeature measuring point; S5, normalizing the optimal feature subsets ofeach optimal feature measuring point and calculating informationentropy, and with the information entropy as an input, predicting afuture trend of the hydraulic turbine set in a healthy state by using ahealth state prediction model constructed based on multilayer gaterecurrent units (GRUs) to obtain predictive information entropy of theacoustic signal of each optimal feature measuring point in the nextpredictive step; S6, obtaining acoustic signal time sequence dataactually acquired from each optimal feature measuring point on thehydraulic turbine set in the next predictive step and calculating actualinformation entropy, and calculating a dynamic tolerance of each optimalfeature measuring point from the predictive information entropy and theactual information entropy; S7, based on the current output condition ofthe hydraulic turbine set, obtaining acoustic signal information entropy(with incipient cavitation present) of the hydraulic turbine set in thenext predictive step through prediction using the pre-constructed SOMnetwork, and calculating a dynamic tolerance alarm threshold of eachoptimal feature measuring point from the predictive information entropyand the acoustic signal information entropy (with incipient cavitationpresent); and S8, comparing a sum of the dynamic tolerances of alloptimal feature measuring points with a sum of the dynamic tolerancealarm thresholds based on a threshold method, and judging whether theincipient cavitation occurs to the hydraulic turbine set; if yes, anincipient cavitation warning is given; otherwise, no incipientcavitation warning is given.
 2. The hydraulic turbine cavitationacoustic signal identification method of claim 1, wherein in S1, themethod of partitioning the acoustic signal time sequence data of eachmeasuring point into multiple acoustic signal subsequences comprises thefollowing steps: S11, performing fixed-step sliding through afixed-sized time window on the acoustic signal time sequence data ofeach measuring point, and extracting an acoustic signal subsequence fromthe time window every time one step is slided by; and S12, normalizingeach acoustic signal subsequence extracted in S11 to obtain a finallyoutputted acoustic signal subsequence.
 3. The hydraulic turbinecavitation acoustic signal identification method of claim 1, wherein theimplementation method of S2 comprises the following steps: S21,inputting the acoustic signal subsequences of all measuring pointsobtained in S1 as an input layer of the SOM neural network, so that theinputted acoustic signal subsequences are divided into differentclusters through unsupervised learning clustering; and S22, for eachcluster clustered in 521, calculating multiple statistical values ofdata points in each acoustic signal subsequence, and then calculating adeviation of each statistical value of different acoustic signalsubsequences in the same cluster; if the deviation of each statisticalvalue corresponding to one cluster is less than the respective deviationthreshold, such cluster is marked as a steady-state cluster; otherwise,such cluster is marked as an unsteady-state cluster.
 4. The hydraulicturbine cavitation acoustic signal identification method of claim 3,wherein the multiple statistical values comprise the mean value, maximumvalue, minimum value and median of the data points in the acousticsignal subsequences, and the deviation is a variance.
 5. The hydraulicturbine cavitation acoustic signal identification method of claim 1,wherein the implementation method of S4 comprises the following steps:S41, performing a first disturbance on each real-time signal subsequencecontained in the steady-state cluster based on a RF algorithm, andcalculating a feature importance index Ψ_(k) of each correspondingmeasuring point according to the results before and after thedisturbance, wherein a calculation formula is as follows:$\Psi_{k} = {\frac{1}{B}{\sum\limits_{b = 1}^{B}\left( {R_{b}^{oob} - R_{bk}^{oob}} \right)}}$where B represents a total number of the real-time signal subsequencescontained in the steady-state cluster, R_(b) ^(oob) represents a numberof correctly classified out-of-bag (OOB) data of a decision-making treebefore the first disturbance is performed on the bth real-time signalsubsequence, and R_(bk) ^(oob) represents a number of correctlyclassified OOB data of a decision-making tree after the firstdisturbance is performed on the bth real-time signal subsequence; S42,based on the feature importance index of each measuring point obtainedin S41, screening optimal feature measuring points which can sensitivelyreflect changes in the operating condition of the hydraulic turbine setfrom the measuring points corresponding to all real-time signalsubsequences in the steady-state cluster; S43, for each optimal featuremeasuring point, performing empirical mode decomposition (EMD) on acorresponding real-time signal subsequence to obtain an equal number offeature subsets, and then performing a second disturbance on eachfeature subset based on the RF algorithm, and calculating a featureimportance index Ψ_(k1) of each corresponding feature subset accordingto the results before and after the disturbance, wherein a calculationformula is as follows:$\Psi_{k1} = {\frac{1}{B^{\prime}}{\sum\limits_{b^{\prime} = 1}^{B^{\prime}}\left( {R_{b\prime}^{\prime{oob}} - R_{b{\prime k}}^{\prime{oob}}} \right)}}$where B′ represents a number of feature subsets of each optimal featuremeasuring point, R′_(b′) ^(oob) represents a number of correctlyclassified OOB data of a decision-making tree before the seconddisturbance is performed on the b′th feature subset, and R′_(b′k) ^(oob)represents a number of correctly classified OOB data of adecision-making tree after the second disturbance is performed on theb′th feature subset; S44, based on the feature importance index of eachfeature subset obtained in S43, screening an equal number of optimalfeature subsets which can sensitively reflect changes in the operatingcondition of the hydraulic turbine set from the feature subsets of eachoptimal feature measuring point.
 6. The hydraulic turbine cavitationacoustic signal identification method of claim 1, wherein theimplementation method of S5 comprises the following steps: S51, for anypth optimal feature measuring point, combining its optimal featuresubsets to form a signal sequence X′=[x_(p,1),x_(p,2), . . . , x_(p,r),. . . , x_(p,R)] with a length R after the feature extraction,normalizing the signal sequence X′ to obtain a signal sequenceY′=[y′_(p,1),y′_(p,2), . . . , y′_(p,R)], and then calculatinginformation entropy y_(p,R) of the normalized signal sequence Y′:$y_{p,R} = {- {\sum\limits_{r = 1}^{R}{y_{p,r}^{\prime}{\lg\left( y_{p,r}^{\prime} \right)}}}}$S52, constructing and training a health state prediction model composedof multilayer GRUs, wherein each GRU is corresponding to an optimalfeature measuring point, configured to predict a future trend ofacoustic signal sequence information entropy at a corresponding optimalfeature measuring point when the hydraulic turbine set is in a healthstate without cavitation; and S53, inputting the information entropyy_(p,R) of each optimal feature measuring point calculated in S51 intothe corresponding GRU in the health state prediction model successively,wherein the GRU outputs predictive information entropy y_(p,h) of theacoustic signal of the corresponding optimal feature measuring point inthe next time step h to be predicted.
 7. The hydraulic turbinecavitation acoustic signal identification method of claim 6, wherein inS6, for any pth optimal feature measuring point, its dynamic toleranceΔy_(p,h) is calculated by the following formula:${\Delta y}_{p,h} = {{{❘\frac{{\hat{y}}_{p,h} - y_{p,h}}{y_{p,h}}❘} \times 100}\%}$where ŷ_(p,h) represents actual information entropy of the acousticsignal time sequence data actually acquired from the pth optimal featuremeasuring point in the time step h.
 8. The hydraulic turbine cavitationacoustic signal identification method of claim 6, wherein theimplementation method of S7 comprises the following steps: S71, based onthe SOM network, constructing a multi-dimensional mapping networkbetween the acoustic signal information entropy of the measuring pointsand the cavitation state of the hydraulic turbine set under differentoutput conditions; S72, determining the current output condition of thehydraulic turbine set, and then predicting acoustic signal informationentropy y _(p,h) (with incipient cavitation present) of the optimalfeature measuring points in the hydraulic turbine set in the time step hby using the multi-dimensional mapping network; and S73, calculating adynamic tolerance alarm threshold Δy _(p,h) according to the acousticsignal information entropy y _(p,h) and the predictive informationentropy y_(p,h) of each optimal feature measuring point, wherein acalculation formula is as follows:${\Delta{\overset{\_}{y}}_{p,h}} = {{{❘\frac{{\overset{\_}{y}}_{p,h} - y_{p,h}}{y_{p,h}}❘} \times 100}{\%.}}$9. The hydraulic turbine cavitation acoustic signal identificationmethod of claim 1, wherein in S8, a comparative analysis is performed byan analytical method of stacked area chart.
 10. The hydraulic turbinecavitation acoustic signal identification method of claim 1, whereinS1-S8 proceed iteratively according to the set step interval when thehydraulic turbine set is operating.